Background and Purpose: We addressed questions about the potential discrepancy between improvements in activity capacity and improvements in activity performance in daily life. We asked whether this discrepancy is: Common in routine, outpatient care, or an artifact of intervention studies? Unique to upper limb (UL) rehabilitation, or is it seen in walking rehabilitation too? Only seen in persons with stroke, or a broader neurorehabilitation problem? Methods: A longitudinal, observational cohort of 156 participants with stroke or Parkinson disease (PD) receiving outpatient rehabilitation at 5 clinics was assessed around admission and monthly during their episode of care. Individual, longitudinal capacity (Action Research Arm Test or walking speed) and performance (wearable sensor measurements of use ratio or steps/day) data were modeled to extract predicted change scores. Simulation methods were used to determine whether an individual's change was greater than 1 standard error. Participants were classified into categories based on whether or not they improved in capacity (C+ or C−) and/or performance (P+ or P−). Results: A majority (59%) were classified as C+P−. Smaller portions of the sample were classified as C+P+ (20%) and C−P− (21%), with 1 participant (<1%) classified as C−P+. The proportions in the C+P− category were significantly larger in the stroke (χ² = 48.7, P < 0.0001) and PD (χ² = 24.3, P < 0.0001) walking subgroups than in the stroke UL subgroup. Discussion and Conclusions: The discrepancy between improvements in capacity and performance is a problem in routine, outpatient neurorehabilitation. If performance information were available, patients and clinicians could act to address it. Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A396).

Background: Wearable movement sensors can measure upper limb (UL) activity, but single variables may not capture the full picture. This study aimed to replicate prior work identifying five multivariate categories of UL activity performance in people with stroke and controls and expand those findings to other UL conditions. Methods: Demographic, self-report, and wearable sensor-based UL activity performance variables were collected from 324 participants (stroke n = 49, multiple sclerosis n = 19, distal UL fracture n = 40, proximal UL pain n = 55, post-breast cancer n = 23, control n = 138). Principal component (PC) analyses (12, 9, 7, or 5 accelerometry input variables) were followed by cluster analyses and numerous assessments of model fit across multiple subsets of the total sample. Results: Two PCs explained 70–90% variance: PC1 (overall UL activity performance) and PC2 (preferred-limb use). A five-variable, five-cluster model was optimal across samples. In comparison to clusters, two PCs and individual accelerometry variables showed higher convergent validity with self-report outcomes of UL activity performance and disability. Conclusions: A five-variable, five-cluster model was replicable and generalizable. Convergent validity data suggest that UL activity performance in daily life may be better conceptualized on a continuum, rather than categorically. These findings highlight a unified, data-driven approach to tracking functional changes across UL conditions and severity of functional deficits.